{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:JXWJTCILEC732KHZUUGC5BHNMS","short_pith_number":"pith:JXWJTCIL","schema_version":"1.0","canonical_sha256":"4dec99890b20bfbd28f9a50c2e84ed64b32709909f3c451c9161003e5afd1227","source":{"kind":"arxiv","id":"1807.00652","version":2},"attestation_state":"computed","paper":{"title":"PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cewu Lu, Mingyang Jiang, Tianqi Zhao, Yiran Wu, Zelin Zhao","submitted_at":"2018-07-02T13:29:47Z","abstract_excerpt":"Recently, 3D understanding research sheds light on extracting features from point cloud directly, which requires effective shape pattern description of point clouds. Inspired by the outstanding 2D shape descriptor SIFT, we design a module called PointSIFT that encodes information of different orientations and is adaptive to scale of shape. Specifically, an orientation-encoding unit is designed to describe eight crucial orientations, and multi-scale representation is achieved by stacking several orientation-encoding units. PointSIFT module can be integrated into various PointNet-based architect"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1807.00652","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-02T13:29:47Z","cross_cats_sorted":[],"title_canon_sha256":"b67571fd32d224775e39ff208e7610ab4d2db8b301e7cbeaaffb5f0062620814","abstract_canon_sha256":"2ca0c08a60de3e09de6a9ae3fde44a3bf4cd8a0cf22c8d5b00b1d1d25515dc43"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:02.012967Z","signature_b64":"zouENuOd+tEG1K5aP7mDJr6iqG7ZlHNeWZP6H8KtcwUwGsRMaRXpf2XDtEK6BrgDUE17+xfGgqmCLeKlXrsLCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4dec99890b20bfbd28f9a50c2e84ed64b32709909f3c451c9161003e5afd1227","last_reissued_at":"2026-05-18T00:00:02.012472Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:02.012472Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PointSIFT: A SIFT-like Network Module for 3D Point Cloud Semantic Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Cewu Lu, Mingyang Jiang, Tianqi Zhao, Yiran Wu, Zelin Zhao","submitted_at":"2018-07-02T13:29:47Z","abstract_excerpt":"Recently, 3D understanding research sheds light on extracting features from point cloud directly, which requires effective shape pattern description of point clouds. Inspired by the outstanding 2D shape descriptor SIFT, we design a module called PointSIFT that encodes information of different orientations and is adaptive to scale of shape. Specifically, an orientation-encoding unit is designed to describe eight crucial orientations, and multi-scale representation is achieved by stacking several orientation-encoding units. PointSIFT module can be integrated into various PointNet-based architect"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.00652","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"1807.00652","created_at":"2026-05-18T00:00:02.012547+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.00652v2","created_at":"2026-05-18T00:00:02.012547+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.00652","created_at":"2026-05-18T00:00:02.012547+00:00"},{"alias_kind":"pith_short_12","alias_value":"JXWJTCILEC73","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_16","alias_value":"JXWJTCILEC732KHZ","created_at":"2026-05-18T12:32:33.847187+00:00"},{"alias_kind":"pith_short_8","alias_value":"JXWJTCIL","created_at":"2026-05-18T12:32:33.847187+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.04444","citing_title":"A review on deep learning techniques for 3D sensed data classification","ref_index":89,"is_internal_anchor":true},{"citing_arxiv_id":"2604.16696","citing_title":"LOD-Net: Locality-Aware 3D Object Detection Using Multi-Scale Transformer Network","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JXWJTCILEC732KHZUUGC5BHNMS","json":"https://pith.science/pith/JXWJTCILEC732KHZUUGC5BHNMS.json","graph_json":"https://pith.science/api/pith-number/JXWJTCILEC732KHZUUGC5BHNMS/graph.json","events_json":"https://pith.science/api/pith-number/JXWJTCILEC732KHZUUGC5BHNMS/events.json","paper":"https://pith.science/paper/JXWJTCIL"},"agent_actions":{"view_html":"https://pith.science/pith/JXWJTCILEC732KHZUUGC5BHNMS","download_json":"https://pith.science/pith/JXWJTCILEC732KHZUUGC5BHNMS.json","view_paper":"https://pith.science/paper/JXWJTCIL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.00652&json=true","fetch_graph":"https://pith.science/api/pith-number/JXWJTCILEC732KHZUUGC5BHNMS/graph.json","fetch_events":"https://pith.science/api/pith-number/JXWJTCILEC732KHZUUGC5BHNMS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JXWJTCILEC732KHZUUGC5BHNMS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JXWJTCILEC732KHZUUGC5BHNMS/action/storage_attestation","attest_author":"https://pith.science/pith/JXWJTCILEC732KHZUUGC5BHNMS/action/author_attestation","sign_citation":"https://pith.science/pith/JXWJTCILEC732KHZUUGC5BHNMS/action/citation_signature","submit_replication":"https://pith.science/pith/JXWJTCILEC732KHZUUGC5BHNMS/action/replication_record"}},"created_at":"2026-05-18T00:00:02.012547+00:00","updated_at":"2026-05-18T00:00:02.012547+00:00"}